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### Introduction: research question, approach, and key empirical findings
- Research question: whether government demand stimulates private investment, examined via firm-level effects of federal procurement contracts in the United States.
- Identification strategy:
  - Link federal procurement contracts (FFATA/OMB data) to firm financials (Compustat).
  - Exploit cross-sectional variation with firm-fixed effects (αi) and period-fixed effects (λt) to isolate unanticipated government demand shocks.
  - Include only contracts awarded in a full and open competition with at least two bidders; drop apparent reporting errors.
- Main empirical results:
  - 1 dollar of federal spending increases capital investment by about 7 cents.
  - Focusing on relatively large contracts (a firm’s largest contracts) raises the effect on investment to about 12 cents.
  - Financially constrained firms display the highest increase in investment following a new government award across different measures of financing constraints (firm size, payout ratio, corporate bond rating).
  - Capital investment is mainly financed via short-term debt: a contract of 1 percent of a firm’s capital increases short-term liabilities by .05 percent.
  - Industry-level analysis: firms’ investment responses aggregate into an increase in industry-level investment, disfavoring a crowding-out hypothesis.
  - Industries with higher dependence on external financing tend to respond more to industry-level government spending.

### Theoretical motivation and hypothesis development
- Mechanism: financial accelerator / collateral-in-advance framework (Bernanke et al. (1996)).
  - When a collateral-in-advance constraint binds, firms face a financing premium that limits demand for inputs and capital investment.
  - New government demand increases firm cash flow, reducing the external financing premium and easing the constraint, thereby increasing firms’ demand for inputs and investment.
- Heterogeneous effect prediction:
  - Constrained firms: additional cash from government contracts relaxes the binding constraint and increases investment.
  - Unconstrained firms: already at the optimum, new contracts do not alter financing conditions and thus have limited effect on investment.
- Relation to DSGE literature: models predict larger spending multipliers when government spending improves financial intermediation or eases borrowing constraints (examples cited: Carrillo and Poilly (2013), Challe and Ragot (2011), Canzoneri et al. (2015)).

### Data: scope, construction, and sample characteristics
- Data sources and sample period:
  - Federal procurement data from the database required by the Federal Funding Accountability and Transparency Act of 2006 (covers time period from 2000 onwards).
  - Firm-level quarterly accounting data from Compustat.
  - Final matched dataset: about 94,000 quarterly observations from 2000Q1 to 2012Q4.
- Procurement data specifics and matching:
  - Procurement database includes contracts > $3,000 with awardee, amount, awarding agency, and award specifics.
  - According to the OMB, aggregate annual value of awarded federal contracts in the United States has been close to 500 billion dollars in the last years.
  - After applying restrictions, total value of contracts is 750 billion dollars of which roughly 620 billion dollars (83 percent) are matched to Compustat firm records.
  - Mean of awarded firm-quarter contracts is about 26 million dollars; distribution is right-skewed (median much lower than the mean).
  - Typical firm in the sample receives between 1 and 5 contracts per year; some firms receive more than 25 contracts per year.
- Construction of firm-level shock variable:
  - Main measure: received amount scaled by lagged property, plant, and equipment (PPE) for each firm-quarter.
  - Benchmark sample: “all contracts”.
  - Alternative definition: “large contracts” = sum of amounts in a quarter that lie above the 90th percentile of all awarded contracts for a firm in a specific year.
- Firm-level variables and controls:
  - Capital investment defined as capital expenditures normalized by lagged PPE.
  - Additional control variables: cash, return on assets, market-to-book ratio.

### Empirical specification, robustness, and anticipation tests
- Empirical approach:
  - Panel investment equation à la Chaney et al. (2009) with firm-fixed effects and quarter fixed effects.
  - Exploits cross-sectional variation across firms within the same quarter.
- Robustness checks:
  - Dynamic models, Arellano-Bond (AB) estimation, alternative contract-size thresholds, DoD-only contracts.
  - Alternative definitions of financing constraints: firm size, payout ratio, bond rating.
- Anticipation checks:
  - Regress Awardit on past daily stock returns Retit−k (k = 1..20); use raw returns and CAPM-adjusted residuals.
  - Finding: no consistent evidence that awarded competitive contracts are anticipated and priced in financial markets.

### Baseline results
- Standard errors clustered at the firm-level.
- Benchmark estimates (Table 3, panel A):
  - Column (1) (no controls or fixed effects): a 1 dollar increase of federal spending increases investment by 7.5 cents.
  - Column (5) (full fixed effects and controls): estimated impact of 6.5 cents per dollar.
- Large-contract sample (Table 3, panel B; contracts above the 90th percentile of awards for a firm-year):
  - With firm- and period-fixed effects and controls (column (10)): 1 dollar of federal spending increases investment by 11 cents.
- Lagged market-to-book value of assets: coefficient positive.

### Financing constraints (heterogeneity)
- Definitions of constrained firms:
  - Size-based: lower (upper) 30th percentile of total assets considered constrained (unconstrained).
  - Payout-based: lower (upper) 30th percentile of payout ratios considered constrained (unconstrained).
  - Credit-rating-based: constrained if S&P domestic long-term issuer credit rating is in the high yield range or lower.
- Results (Table 4):
  - Small vs large firms:
    - Panel A (all contracts): small firms increase investment by 7.5 cents per dollar; no effect for large firms.
    - Panel B (large contracts only): small-firm effect rises to 15 cents per dollar; no effect for large firms.
  - Payout ratio (low dividends): increase in investment of 7.4 cents (all contracts) and 12.7 cents (large contracts).
  - Credit rating (high-yield or no rating): increase in investment of 6.7 cents (all contracts) and 11.3 cents (large contracts).
  - Firms with an investment grade rating: no significant effect.

### External borrowing responses
- Dependent variables: growth rates of total debt or short-term debt.
- All firms (Table 5):
  - Winning an amount of 1 percent of capital is associated with short-term debt growth of .06 percentage points (using all contracts or large contracts only).
  - Effect on total liabilities is slightly smaller.
- Constrained vs unconstrained firms (Table 6):
  - Constrained firms increase debt in response to winning government contracts across measures and specifications.
  - Unconstrained firms: smaller, less precise, and generally nonsignificant point estimates.
- Interpretation: federal contracts increase firms’ capacity to obtain external funding.

### Industry-level results
- Aggregation: Compustat universe, industries at 4-digit SIC, quarterly industry investment and government demand shocks.
- Industry vs firm-level comparison:
  - Industry-level investment effect is similar in magnitude to firm-level effect; example: firm-level benchmark ~6 cents per dollar; industry-level estimate ~7 cents per dollar (column (1) in table 8).
  - Results do not support crowding-out at the industry level.
- Financial dependence split:
  - Industries with higher external financial dependence show a slightly higher rise in investment, but the difference is small.
- Caveat: cross-industry crowding-out cannot be ruled out.

### Anticipation and robustness (Section V)
- Anticipation tests (Tables 8–9):
  - No consistent evidence that awarded contracts are anticipated by financial markets.
  - Post-award trading strategy (buy winners, short non-winners): average monthly return .5 percent (approximately 6 percent per year); returns robust to CAPM, Fama-French three-factor, and four-factor models.
- Robustness to model specification (Table 10):
  - Dynamic panel specifications with two lags: long-run multipliers 7.3 cents and 12.2 cents per dollar (columns (2) and (5)).
  - Arellano-Bond (AB) estimation: long-run impact 4 cents per dollar (all contracts) and 11 cents per dollar (large contracts).
  - Overall estimates broadly similar across static, dynamic, and AB specifications.
- Robustness to contract selection (Tables 11–12):
  - Alternative contract-size thresholds (>$50,000 or >$500,000): coefficients similar to benchmark (~6 cents per dollar).
  - Department of Defense (DoD) contracts only: firms increase capital investment by 8 cents per dollar of a DoD contract; increase concentrated in financially constrained firms.
- Conclusion of robustness checks:
  - Results robust to model specification and contract selection; main patterns persist (6–11 cents per dollar for constrained firms; larger effects for large contracts; debt increases for constrained firms).

### Key quantitative findings (selected)
- Baseline: 1 dollar of federal spending increases firm capital investment by 7.5 cents (no controls) and 6.5 cents (full controls).
- Large contracts (90th percentile): up to 11 cents per dollar (full controls).
- Small firms: 7.5 cents per dollar (all contracts); 15 cents per dollar (large contracts).
- Low dividend firms: 7.4 cents (all contracts); 12.7 cents (large contracts).
- High-yield/no-rating firms: 6.7 cents (all contracts); 11.3 cents (large contracts).
- Debt response: winning 1 percent of capital associated with short-term debt growth of .06 percentage points.
- Strategy return: average monthly .5 percent (approximately 6 percent per year).

### Overall conclusion
- Financially constrained firms react to new government demand by raising capital investment.
- Average response of capital investment lies between 6 and 11 cents per dollar of government demand, with substantial heterogeneity concentrated in financially constrained firms.
- Evidence indicates government contracts facilitate firms’ access to external financing.
- Industry-level aggregation shows an almost one-to-one pass-through to industry capital investment, challenging the traditional crowding-out hypothesis at the industry level while leaving open potential cross-industry spillovers for future research.

### Appendix A: variable definitions and matching procedure (selected)
- Award: obliged amount of a federal contract normalized by lagged PPE (ppentq).
- Capital investment: capxy normalized by lagged PPE (ppentq).
- Cash: (iby + dpq) normalized by lagged PPE (ppentq).
- Market to book value of assets (MkttoBook): market value of assets normalized by book value (atq).
- Size: log of total assets (atq).
- RoA: ((oibdpq - dpq) divided by atq).
- Ex ante measures of credit constraints (firm-level):
  - Firm Size: lower (upper) 30th percentile of total assets = constrained (unconstrained).
  - Dividend payments (payout ratio): lower (upper) 30th percentile of payout ratios = constrained (unconstrained); payout ratio = (dvpsxq + cshopq) divided by oibdpq.
  - Corporate credit rating: constrained if S&P domestic long term issuer credit rating (splticrm) is BBB or lower.
- Financial dependency (industry-level): industry-median (4-digit SIC) of capital expenditures minus cash flows over capital expenditures.
- Returns: Ret is daily stock returns from CRSP.
- Short-term debt: growth rate of short-term debt (ltq - lltq).
- Total debt: growth rate of total debt (ltq).
- Matching procedure:
  - FFATA procurement data collapsed at parent DUNS level; 566,271 different DUNS numbers; matched to Bureau van Dijk’s Orbis/Osiris resulting in matched company records and tickers used to link to financial data.

*Source: _wp1660 - References (content extracted from the provided PDF chapter).*

### References .............................................................................................................

### _wp1660 - References

### Introduction: research question, approach, and key empirical findings
- Research question: whether government demand stimulates private investment, examined via firm-level effects of federal procurement contracts in the United States.
- Identification strategy: link federal procurement contracts (FFATA/OMB data) to firm financials (Compustat) and exploit cross-sectional variation with firm-fixed effects and period-fixed effects to isolate unanticipated government demand shocks.
- Contract selection to capture unanticipated shocks:
  - Include only contracts awarded in a full and open competition with at least two bidders.
  - Drop apparent reporting errors (e.g., signing date after effective date).
- Main empirical results:
  - 1 dollar of federal spending increases capital investment by about 7 cents.
  - Focusing on relatively large contracts (a firm’s largest contracts) raises the effect on investment to about 12 cents.
  - Financially constrained firms display the highest increase in investment following a new government award across different measures of financing constraints (firm size, payout ratio, corporate bond rating).
  - Capital investment is mainly financed via short-term debt: a contract of 1 percent of a firm’s capital increases short-term liabilities by .05 percent.
  - Industry-level analysis: firms’ investment responses aggregate into an increase in industry-level investment, disfavoring a crowding-out hypothesis.
  - Industries with higher dependence on external financing tend to respond more to industry-level government spending.

### Theoretical motivation and hypothesis development
- Mechanism: financial accelerator / collateral-in-advance framework (Bernanke et al. (1996)).
  - When a collateral-in-advance constraint binds, firms face a financing premium that limits demand for inputs and capital investment.
  - New government demand increases firm cash flow, reducing the external financing premium and easing the constraint, thereby increasing firms’ demand for inputs and investment.
- Heterogeneous effect:
  - Constrained firms: additional cash from government contracts relaxes the binding constraint and increases investment.
  - Unconstrained firms: already at the optimum, new contracts do not alter financing conditions and thus have limited effect on investment.
- Relation to DSGE literature: several models predict larger spending multipliers when government spending improves financial intermediation or eases borrowing constraints (examples cited include Carrillo and Poilly (2013), Challe and Ragot (2011), Canzoneri et al. (2015)).

### Data: scope, construction, and sample characteristics
- Data sources and sample period:
  - Federal procurement data from the database required by the Federal Funding Accountability and Transparency Act of 2006 (covers time period from 2000 onwards).
  - Firm-level quarterly accounting data from Compustat.
  - Final matched dataset: about 94,000 quarterly observations from 2000Q1 to 2012Q4.
- Federal procurement data specifics:
  - The procurement database includes contract-level transactions for each federal award of more than $3,000 with information on awardee, amount, awarding agency, and award specifics (e.g., whether the award was determined in a competitive bidding process).
  - According to the OMB, the aggregate annual value of awarded federal contracts in the United States has been close to 500 billion dollars in the last years.
  - After applying restrictions, the total value of contracts is 750 billion dollars of which roughly 620 billion dollars (83 percent) are matched to Compustat firm records.
  - The mean of awarded firm-quarter contracts is about 26 million dollars; the distribution is right-skewed (median much lower than the mean).
  - Typical firm in the sample receives between 1 and 5 contracts per year; some firms receive more than 25 contracts per year.
- Construction of firm-level shock variable:
  - Main measure: received amount scaled by lagged property, plant, and equipment (PPE) for each firm-quarter.
  - Benchmark sample: “all contracts” (no additional exclusion beyond the described restrictions).
  - Alternative definition: “large contracts” defined as the sum of amounts in a quarter that lie above the 90th percentile of all awarded contracts for a firm in a specific year.
- Firm-level variables and controls:
  - Capital investment defined as capital expenditures normalized by lagged PPE.
  - Additional control variables constructed: cash, return on assets, market-to-book ratio.

### Empirical specification, robustness, and anticipation tests
- Empirical approach:
  - Panel investment equation à la Chaney et al. (2009) with firm-fixed effects to control for time-invariant heterogeneity and period-fixed effects for each quarter to capture time-varying macroeconomic factors common to all firms (e.g., interest rate changes, legislation, aggregate shocks).
  - Exploits cross-sectional variation across firms within the same quarter, allowing inclusion of quarter fixed effects unlike aggregate country-level time series studies.
- Robustness checks:
  - Consideration of dynamic models and various subsets of contracts.
  - Examination of alternative definitions of financing constraints (size, payout ratio, bond rating).
- Anticipation checks:
  - Test for anticipation by examining stock market returns; finding: no evidence that competitive federal contracts are anticipated and priced in financial markets, supporting the interpretation that identified contract awards capture unexpected shocks.

### Policy-relevant implications
- Fiscal interventions that increase firm-specific cash flows can relax external financing constraints and spur private capital investment, especially among financially constrained firms.
- Effective fiscal policy for stimulating investment should address disparities in firms’ access to external funding, consistent with a role for credit market conditions in the propagation of shocks.
- Micro-level procurement shocks provide a transmission channel through which government spending can influence aggregate private investment and thus contribute to the aggregate spending multiplier.

*Source: _wp1660 - References (content extracted from the provided PDF chapter).*

### Appendix 8.2 describes all variables in detail. Table 1 displays descriptive statistics of the main

### _wp1660 - Appendix 8.2 describes all variables in detail. Table 1 displays descriptive statistics of the main

### Data and variables
- Appendix 8.2 describes all variables in detail.
- Table 1 displays descriptive statistics of the main accounting variables used in the study.
- Awardit is defined as the sum of the values of all awarded government contracts to firm i in quarter t.
- Industry share of number of contracts (axis labels present): 0.05.1.15

### Industry distribution of contract recipients
- Contract recipients operate in various sectors of the economy (SIC, 2 digit).
- Major recipients of awards are manufacturing (including industrial machinery and computer equipment) and business services.
- Listed sectors receiving contracts (as reported):
  - Wholesale Trade - Nondurable Goods
  - Wholesale Trade - Durable Goods
  - Transportation by Air
  - Transportation Equipment
  - Petroleum Refining and Related Industries
  - Paper and Allied Products
  - Miscellaneous Retail
  - Mesr/Anlyz/Cntrl Instrmnts; Photo/Med/Opt Gds; Watchs/Clocks
  - Industrial and Commercial Machinery and Computer Equipment
  - Heavy Cnstrctn, Except Building Construction - Contractors
  - Health Services
  - Furniture and Fixtures
  - Food and Kindred Products
  - Fabricated Metal Prdcts, Except Machinery & Transport Eqpmnt
  - Engineering, Accounting, Research, Management & Related Svcs
  - Electronic, Elctrcl Eqpmnt & Cmpnts, Excpt Computer Eqpmnt
  - Electric, Gas and Sanitary Services
  - Communications
  - Chemicals and Allied Products
  - Business Services

### Definitions of financially constrained firms
- Three alternative definitions (following literature such as Almeida et al. (2004) and Almeida and Campello (2007)):
  - Size-based: firms ranked by total assets; those in the lower (upper) 30th percentile are considered constrained (unconstrained).
  - Payout-based: firms ranked by payout ratios; those in the lower (upper) 30th percentile are considered financially constrained (unconstrained).
  - Credit-rating-based: firms are constrained if the S&P domestic long-term issuer credit rating is in the high yield range or lower.

### Empirical specification
- Reduced-form specification estimated (equation (2)):
  - Awardit = βTreatit + αi + λt + Xit + uit  (notation and exact equation structure appears in source)
- Estimation details:
  - Firm-fixed effects (αi) included.
  - Time-fixed effects (λt) included.
  - Control variables (Xit) included.
- Samples:
  - Model estimated using sample of all firms and subsamples of constrained and non-constrained firms.
  - Models run for all contracts and for a sample including only relatively large contracts.

### Identification and sources of variation
- Coefficient of interest: β; expectation that the marginal effect is larger for financially constrained firms than for unconstrained firms.
- Two identified sources of variation for β:
  - Cross-sectional: differences across firms that won contracts and firms that did not win a contract in a given quarter t.
  - Within-firm: periods when firm i has not been awarded contracts versus periods when firm i has been awarded a contract.

### Controls and theoretical guidance
- Vector of controls Xit follows Chaney et al. (2012) and includes:
  - Cash
  - Lagged market-to-book value of assets (Mkt to Book)
  - Lagged returns on assets (RoA)
  - Lagged size of the firm
- Industry-specific time-invariant unobserved heterogeneous effects (e.g., market structure) are captured by firm-fixed effects αi.
- Period-fixed effects λt capture factors common across firms in a quarter and allow for potentially time-dependent impacts of spending (e.g., countercyclical external finance premium during financial turmoil; quality of federal spending in IT sector declines at the end of the fiscal year).

### Endogeneity considerations
- Analysis is conditional on the observation of winning a contract.
- Potential endogeneity concern: signing a contract may be predictable and correlated with error term uit.
- Mitigating factor: all contracts in the sample were awarded through a competitive procedure, reducing concern about predictability of contract awards.

*Source: _wp1660 - Appendix 8.2 describes all variables in detail. Table 1 displays descriptive statistics of the main*

### section 5, we consider this issue by testing whether stock returns can predict subsequent contracts

### _wp1660 - section 5, we consider this issue by testing whether stock returns can predict subsequent contracts

### Baseline Results
- All standard errors are clustered at the firm-level.
- Benchmark estimates (Table 3, panel A):
  - Column (1) (no controls or fixed effects): a 1 dollar increase of federal spending increases investment by 7.5 cents.
  - Columns (2)–(4) (various firm- and/or period-fixed effects): coefficients remain statistically significant at the 1 percent level.
  - Column (5) (full fixed effects and controls): estimated impact of 6.5 cents per dollar.
- Large-contract sample (Table 3, panel B; contracts above the 90th percentile of awards for a firm-year):
  - Estimates larger than panel A.
  - With firm- and period-fixed effects and controls (column (10)): 1 dollar of federal spending increases investment by 11 cents.
- Lagged market-to-book value of assets: coefficient positive.

### Financing Constraints (Table 4)
- Definition: financially constrained firms are those that are either small, pay low dividends, or have a low corporate credit rating.
- Small vs large firms:
  - Panel A (all contracts): small firms increase investment by 7.5 cents per dollar of government awards; no effect for large firms.
  - Panel B (large contracts only): small-firm effect rises to 15 cents per dollar; no effect for large firms.
- Alternative indicators:
  - Payout ratio (low dividends): increase in investment of 7.4 cents (all contracts) and 12.7 cents (large contracts).
  - Credit rating (high-yield or no rating): increase in investment of 6.7 cents (all contracts) and 11.3 cents (large contracts).
  - Firms with an investment grade rating: no significant effect.

### External Borrowing (equation (3) and Tables 5–6)
- Dependent variable in debt regressions: growth rates of total debt or short-term debt.
- All firms (Table 5):
  - Winning an amount of 1 percent of capital is associated with short-term debt growth of .06 percentage points (using all contracts or large contracts only).
  - Effect on total liabilities is slightly smaller.
- Constrained vs unconstrained firms (Table 6):
  - Constrained firms increase debt in response to winning government contracts across measures and specifications.
  - Unconstrained firms: smaller, less precise, and generally nonsignificant point estimates.
- Interpretation: federal contracts increase firms’ capacity to obtain external funding.

### Industry-Level Results (Tables 7–8)
- Aggregation: universe of Compustat firms, industries at 4-digit SIC, quarterly industry investment and government demand shocks.
- Industry vs firm-level comparison:
  - Industry-level investment effect is similar in magnitude to firm-level effect, not supporting crowding-out at the industry level.
  - Example: firm-level benchmark ~6 cents per dollar; industry-level estimate ~7 cents per dollar (column (1) in table 8), statistically indistinguishable.
- Financial dependence split (industry median of capital expenditures minus cash flows over capital expenditures):
  - Industries with higher external financial dependence show a slightly higher rise in investment, but the difference is small.
- Conclusion: results suggest government spending can increase aggregate private investment and do not support a general crowding-out effect at the industry level (though cross-industry crowding-out cannot be ruled out).

### Anticipation and Robustness (Section V)
A. Are Competitive Contracts Anticipated? (equations (4)–(5), Table 8, Table 9)
- Test: regress Awardit on past daily stock returns Retit−k (k = 1..20); use raw returns and CAPM-adjusted residuals.
- Samples: (i) all days between 2000 and 2012; (ii) only days when the firm won a contract.
- Results (Table 8, Panel A and B):
  - No consistent evidence that awarded contracts are anticipated by financial markets.
  - F-statistics for joint significance of θk indicate signing a contract is not anticipated (except column (1)).
  - Panel B (quarterly returns) supports the no-anticipation conclusion.
- Post-award stock performance (Table 9):
  - Strategy: buy firms that won a contract in month t, short firms that did not; update monthly.
  - Average monthly strategy return: .5 percent (approximately 6 percent per year).
  - Strategy return is virtually unaffected by CAPM, Fama-French three-factor, and four-factor (momentum) models; factor loadings are small. Interpretation: markets do not anticipate winners, but reflect contract awards after they occur.

B. Robustness to Model Specification (Table 10)
- Dynamic panel specifications including two lags of the dependent variable:
  - Long-run multipliers computed adjusting for autoregressive structure.
  - Effects: 7.3 cents and 12.2 cents per dollar (columns (2) and (5)), slightly higher but similar to static benchmarks.
- Arellano-Bond (AB) estimation (columns (3) and (6)):
  - Long-run impact: 4 cents per dollar (all contracts) and 11 cents per dollar (large contracts).
- Overall robustness: estimates broadly similar across static, dynamic, and AB specifications.

C. Robustness to Contract Selection (Tables 11–12)
- Alternative contract-size thresholds:
  - Contracts > USD 50,000 or > USD 500,000: coefficient estimates similar to benchmark (~6 cents per dollar).
- Department of Defense (DoD) contracts only:
  - Effect slightly higher: firms increase capital investment by 8 cents per dollar of a DoD contract (columns (5) and (6) of table 11).
  - Increase concentrated in financially constrained firms (panel C of table 12).
- Panels A–C of Table 12 (contracts > USD 50,000; > USD 500,000; DoD-only) confirm concentration of effects in constrained firms across size, payout, and credit-rating splits.

D. Conclusion of robustness checks
- Results are robust to model specification and contract selection.
- Main patterns (6–11 cents per dollar for constrained firms; larger effects for large contracts; debt increases for constrained firms) persist across multiple robustness exercises.

### Key Quantitative Findings (selected)
- Baseline: 1 dollar of federal spending increases firm capital investment by 7.5 cents (no controls) and 6.5 cents (full controls).
- Large contracts (90th percentile): up to 11 cents per dollar (full controls).
- Small firms: 7.5 cents per dollar (all contracts); 15 cents per dollar (large contracts).
- Low dividend firms: 7.4 cents (all contracts); 12.7 cents (large contracts).
- High-yield/no-rating firms: 6.7 cents (all contracts); 11.3 cents (large contracts).
- Debt response: winning 1 percent of capital associated with short-term debt growth of .06 percentage points.
- Strategy return: average monthly .5 percent (approximately 6 percent per year).

### Overall Conclusion (Section D)
- Financially constrained firms react to new government demand by raising capital investment.
- Average response of capital investment lies between 6 and 11 cents per dollar of government demand, with substantial heterogeneity concentrated in financially constrained firms.
- Evidence indicates government contracts facilitate firms’ access to external financing.
- Industry-level aggregation shows an almost one-to-one pass-through to industry capital investment, challenging the traditional crowding-out hypothesis at the industry level while leaving open potential cross-industry spillovers for future research.

*Source: _wp1660 - section 5, we consider this issue by testing whether stock returns can predict subsequent contracts; canonical URL: https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2016/_wp1660.pdf*

### References

### _wp1660 - References

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### Appendix A: Supplementary Online Appendix

#### Variable Definitions
- Award: is the obliged amount of a federal contract obtained from the database of the Office of Management and Budget normalized by the lagged book value of properties, plant and equipment, PPE, obtained form Compustat (ppentq).
- Capital investment: is capital expenditure (capxy) normalized by the lagged book value of properties, plant and equipment, PPE, (ppentq).
- Cash: is income before extraordinary items + depreciation and amortization (iby + dpq) normalized by lagged book value of value of properties, plant and equipment, PPE, (ppentq).
- Market to book value of assets: MkttoBook is the market value of assets (atq - ceqq + (cshoq × prccq) - txdbq) normalized by their book value (atq).
- Size: is the log of total assets (atq).
- RoA: is return on assets ((oibdpq - dpq) divided by atq).
- Ex ante measures of credit constraints (Firm-level):
  - Firm Size: In every quarter, we rank firms based on their total assets and consider those in the lower (upper) 30th percentile as constrained (unconstrained).
  - Dividend payments (payout ratio): In every quarter, we rank firms based on their payout ratios and consider those in the lower (upper) 30th percentile as constrained (unconstrained). The payout ratio is (dvpsxq + cshopq) divided by oibdpq.
  - Corporate credit rating: We consider firms to be constrained if the S&P domestic long term issuer credit rating (splticrm is BBB or lower.
- Financial dependency (industry-level): As in Rajan and Zingales (1998), we compute the industry-median (at the 4 digits, Standard Industrial Classification system) of capital expenditures minus cash flows over capital expenditures.
- Returns: Ret is a daily series of stock returns from CRSP.
- Short-term debt: The growth rate of short-term debt (ltq - lltq).
- Total debt: The growth rate of total debt (ltq).

#### Matching Procedure between Procurement Contracts and Compustat
- The Federal Funding Accountability and Transparency Act (FFATA) requires the disclosure of information on entities that receive Federal awards. These include types of contracts, grants, loans, and other types of spending.
- The data are at the plant level; company name, address and telephone numbers are given. In addition, each entry has a DUNS number which is a plant-level company identifier that is used by some government agencies. The DUNS number of the parent company is also part of the data set. It is called parentdunsnumber and is our primary identifier for companies. We collapse all data at the parent company level and we extract a list of all unique parent DUNS numbers that are part of the dataset, a total of 566,271 different DUNS numbers.
- Only few data sets have the DUNS number as an identifier. One dataset that includes DUNS numbers is Bureau van Dijk’s Orbis database. We uploaded our list of unique parent DUNS numbers to Orbis and were able to match 313,935 company records. For the companies that we could match, we downloaded the company name (for checking purposes), a BvD ID number, a BvD account number and the ticker symbol alongside with the DUNS number. Ticker information was not available for 310,523 companies, which is about the size that we expected.
- We could use the ticker directly in a standard financial database such as CRSP but we are careful to note that tickers might change over time. We therefore included one more step.
- Another BvD product, Osiris provides financial accounting data for publicly listed companies around the world. Osiris also includes each company’s current ticker for each year.
- The OS ID number is identical to the BvD account number and can be used to access the data. We uploaded our list of BvD accout numbers to Osiris (a total of 312,586 because 1349 observations do not have a unique DUNS - BvD account number match), and received data for a total of 2754 companies, by OS ID number and year from 2000 to 2012. We also downloaded the current ticker, the current CIK number, the current stock exchange and the listing status. We obtained 32,106 valid observations over the 13 years.
- Footnote: S&P Capital IQ claims to have it but since we did not have access to their dataset, we could not validate how helpful it is.

*Source: _wp1660 - References*

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_Source: https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2016/_wp1660.pdf_